About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
M-6: Design of Casting-friendly TiAl Alloy by Artificial Neuron Network |
Author(s) |
Yu-Jen Tseng, Hong-Yuan Sun, Yi-Hsuan Sun, Cheng-Hsueh Chiang, Hung-Wei Yen |
On-Site Speaker (Planned) |
Yu-Jen Tseng |
Abstract Scope |
TiAl alloys have been considered promising superalloys for aerospace and automobile industry. However, its fluidity limits its castability, alloy design and applications. Moreover, casting practice or related research is not attractive to young scientists. This study introduces artificial neuron network to disclose relationship between alloy composition and fluidity, which measured by spiral fluidity tests. Alloying features such as latent heat, peritectic solid fraction, superheat, and kinematic viscosity are obtained from calculated phase diagram and Scheil solidification. These features and processing variables act as inputs to build an ensemble model with single hidden layer. This model named feature model, which is coded with the relationship between alloy features and fluidity. Then, another model named composition model is trained with virtual data given by feature model, which provides relationship between composition and fluidity. This work demonstrates an approach to predict fluidity of TiAl and an integration of casting practice and artificial intelligence. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, High-Temperature Materials, Solidification |